A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images
Abstract Neoadjuvant chemotherapy (NAC) is a systemic and systematic chemotherapy regimen for breast cancer patients before surgery. However, NAC is not effective for everyone, and the process is excruciating. Therefore, accurate early prediction of the efficacy of NAC is essential for the clinical...
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| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-024-01543-7 |
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| author | Jiang Xie Jinzhu Wei Huachan Shi Zhe Lin Jinsong Lu Xueqing Zhang Caifeng Wan |
| author_facet | Jiang Xie Jinzhu Wei Huachan Shi Zhe Lin Jinsong Lu Xueqing Zhang Caifeng Wan |
| author_sort | Jiang Xie |
| collection | DOAJ |
| description | Abstract Neoadjuvant chemotherapy (NAC) is a systemic and systematic chemotherapy regimen for breast cancer patients before surgery. However, NAC is not effective for everyone, and the process is excruciating. Therefore, accurate early prediction of the efficacy of NAC is essential for the clinical diagnosis and treatment of patients. In this study, a novel convolutional neural network model with bimodal layer-wise feature fusion module (BLFFM) and temporal hybrid attention module (THAM) is proposed, which uses multistage bimodal ultrasound images as input for early prediction of the efficacy of neoadjuvant chemotherapy in locally advanced breast cancer (LABC) patients. The BLFFM can effectively mine the highly complex correlation and complementary feature information between gray-scale ultrasound (GUS) and color Doppler blood flow imaging (CDFI). The THAM is able to focus on key features of lesion progression before and after one cycle of NAC. The GUS and CDFI videos of 101 patients collected from cooperative medical institutions were preprocessed to obtain 3000 sets of multistage bimodal ultrasound image combinations for experiments. The experimental results show that the proposed model is effective and outperforms the compared models. The code will be published on the https://github.com/jinzhuwei/BLTA-CNN . |
| format | Article |
| id | doaj-art-62548186cd7446e9a5359dc8e627c259 |
| institution | Kabale University |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-62548186cd7446e9a5359dc8e627c2592025-01-26T12:57:58ZengBMCBMC Medical Imaging1471-23422025-01-0125111410.1186/s12880-024-01543-7A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound imagesJiang Xie0Jinzhu Wei1Huachan Shi2Zhe Lin3Jinsong Lu4Xueqing Zhang5Caifeng Wan6School of Computer Engineering and Science, Shanghai UniversitySchool of Medicine, Shanghai UniversitySchool of Computer Engineering and Science, Shanghai UniversitySchool of Computer Engineering and Science, Shanghai UniversityDepartment of Ultrasound, Renji Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Pathology, Renji Hospital Affiliated to Shanghai Jiao Tong University School of MedicineDepartment of Ultrasound, Renji Hospital, Shanghai Jiao Tong University School of MedicineAbstract Neoadjuvant chemotherapy (NAC) is a systemic and systematic chemotherapy regimen for breast cancer patients before surgery. However, NAC is not effective for everyone, and the process is excruciating. Therefore, accurate early prediction of the efficacy of NAC is essential for the clinical diagnosis and treatment of patients. In this study, a novel convolutional neural network model with bimodal layer-wise feature fusion module (BLFFM) and temporal hybrid attention module (THAM) is proposed, which uses multistage bimodal ultrasound images as input for early prediction of the efficacy of neoadjuvant chemotherapy in locally advanced breast cancer (LABC) patients. The BLFFM can effectively mine the highly complex correlation and complementary feature information between gray-scale ultrasound (GUS) and color Doppler blood flow imaging (CDFI). The THAM is able to focus on key features of lesion progression before and after one cycle of NAC. The GUS and CDFI videos of 101 patients collected from cooperative medical institutions were preprocessed to obtain 3000 sets of multistage bimodal ultrasound image combinations for experiments. The experimental results show that the proposed model is effective and outperforms the compared models. The code will be published on the https://github.com/jinzhuwei/BLTA-CNN .https://doi.org/10.1186/s12880-024-01543-7Deep learningMultistage bimodal ultrasound imagesBreast cancerNeoadjuvant chemotherapy |
| spellingShingle | Jiang Xie Jinzhu Wei Huachan Shi Zhe Lin Jinsong Lu Xueqing Zhang Caifeng Wan A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images BMC Medical Imaging Deep learning Multistage bimodal ultrasound images Breast cancer Neoadjuvant chemotherapy |
| title | A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images |
| title_full | A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images |
| title_fullStr | A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images |
| title_full_unstemmed | A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images |
| title_short | A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images |
| title_sort | deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images |
| topic | Deep learning Multistage bimodal ultrasound images Breast cancer Neoadjuvant chemotherapy |
| url | https://doi.org/10.1186/s12880-024-01543-7 |
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